Advances in automated speech recognition (ASR), has led to increasing interest in spoken language understanding (SLU). A challenge in large vocabulary spoken language understanding is robustness to compensate for ASR errors. Speech recognition is not perfect, and every user understands that occasional recognition mistakes are a fact of life. From a user's perspective, easiness of correction of recognition mistakes has a substantial impact on an overall experience of a user when speech recognition applications or programs are used. It is with respect to this general technical environment that the present application is directed.